from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType





@dataclass
class UpperCAmelCase		:


				'''simple docstring'''






				lowerCamelCase_        =   42
				lowerCamelCase_        =   None
				# Automatically constructed
				lowerCamelCase_        =   "dict"
				lowerCamelCase_        =   None
				lowerCamelCase_        =   field(default='''Translation'''   ,			init=__A   ,			repr=__A    )
				def __call__(		self   ):



						"""simple docstring"""





						return pa.struct({lang: pa.string() for lang in sorted(self.languages   )}   )







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						from .features import Value

						return {k: Value('string'   ) for k in sorted(self.languages   )}





@dataclass
class UpperCAmelCase		:


				'''simple docstring'''






				lowerCamelCase_        =   None
				lowerCamelCase_        =   None
				lowerCamelCase_        =   None
				# Automatically constructed
				lowerCamelCase_        =   "dict"
				lowerCamelCase_        =   None
				lowerCamelCase_        =   field(default='''TranslationVariableLanguages'''   ,			init=__A   ,			repr=__A    )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[int]      = sorted(set(self.languages   )   ) if self.languages else None
						A_						:							Dict      = len(self.languages   ) if self.languages else None
				def __call__(		self   ):



						"""simple docstring"""





						return pa.struct({'language': pa.list_(pa.string()   ), 'translation': pa.list_(pa.string()   )}   )
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							str      = set(self.languages   )
						if self.languages and set(lowercase   ) - lang_set:
								raise ValueError(
								    F'''Some languages in example ({', '.join(sorted(set(lowercase   ) - lang_set   )   )}) are not in valid set ({', '.join(lowercase   )}).'''   )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						A_						:							Any      = []
						for lang, text in translation_dict.items():
								if isinstance(lowercase						,      lowercase   ):
										translation_tuples.append((lang, text)   )
								else:
										translation_tuples.extend([(lang, el) for el in text]   )

        # Ensure translations are in ascending order by language code.
						A_  ,     A_						:							str      = zip(*sorted(lowercase   )   )

						return {"language": languages, "translation": translations}







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						from .features import Sequence, Value

						return {
						    "language": Sequence(Value('string'   )   ),
						    "translation": Sequence(Value('string'   )   ),
						}







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class UpperCAmelCase		(   __A   ,			__A   ,			__A    ):


				'''simple docstring'''






				@register_to_config
				def __init__(		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase = False						,      ):



						"""simple docstring"""





						super().__init__()

						A_						:							Optional[int]      = nn.Embedding(lowercase						,      lowercase   )

						A_						:							Any      = nn.Embedding(lowercase						,      lowercase   )
						A_						:							int      = False

						A_						:							Tuple      = nn.Dropout(p=lowercase   )

						A_						:							List[Any]      = TaConfig(
						    vocab_size=lowercase						,      d_model=lowercase						,      num_heads=lowercase						,      d_kv=lowercase						,      d_ff=lowercase						,      dropout_rate=lowercase						,      feed_forward_proj=lowercase						,      is_decoder=lowercase						,      is_encoder_decoder=lowercase						,      )

						A_						:							List[str]      = nn.ModuleList()
						for lyr_num in range(lowercase   ):
								A_						:							Dict      = TaBlock(lowercase   )
								self.encoders.append(lowercase   )

						A_						:							Any      = TaLayerNorm(lowercase   )
						A_						:							Optional[int]      = nn.Dropout(p=lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase   ):



						"""simple docstring"""





						A_						:							Any      = self.token_embedder(lowercase   )

						A_						:							List[Any]      = encoder_input_tokens.shape[1]
						A_						:							int      = torch.arange(lowercase						,      device=encoder_input_tokens.device   )
						x += self.position_encoding(lowercase   )

						A_						:							Dict      = self.dropout_pre(lowercase   )

						# inverted the attention mask
						A_						:							List[Any]      = encoder_input_tokens.size()
						A_						:							Dict      = self.get_extended_attention_mask(lowercase						,      lowercase   )

						for lyr in self.encoders:
								A_						:							List[str]      = lyr(lowercase						,      lowercase   )[0]
						A_						:							Optional[int]      = self.layer_norm(lowercase   )

						return self.dropout_post(lowercase   ), encoder_inputs_mask






